Client influence on property valuation has been an emerging theme of behavioural research in the real estate discipline. Studies on valuers’ decision-making behaviour imply that client influence is an important source of judgemental bias. Academic interest in client influence research has evolved from identifying the existence of client pressure to studies that explain the mechanism of client influence. A questionnaire survey was administered to valuers to measure their perception with regard to factors affecting client influence in Malaysia. The effect of client size and size of value adjustment requested by clients on valuation were also tested in a behavioural experiment. The survey revealed that valuers in Malaysia perceived client characteristics and valuer characteristics as some of the most important factors affecting client influence on valuations. It was found that factors such as type of client, size of client, integrity of valuer and experience of valuer could potentially impact on the amount and type of influence exerted on valuations. The results of the logistic regression model indicated that neither the client size nor magnitude of value adjustment requested by client affected the decisions of valuers to alter valuation outcome.

Most of the previous studies on causes of valuation variance have concentrated on non-statutory valuation, with little attention to statutory valuation in both developed and developing countries, leaving a gap in the body of knowledge in this regard. Purposive sampling was adopted to select samples from registered estate surveyors and valuers in Kwara State, Nigeria. The data collection was done through a survey questionnaire given to 33 valuers and the Relative Importance Index (RII) was used to analyse the data collected. Findings showed that factors that fell within the range index of significant factors (0.841 to 0.979) are: experience in rating valuation, comprehensiveness of the law, unrealistic valuation assumption and availability of market indices for the input variables. Other significant factors are explicitness of the law, integrity of the valuer, valuer negligence, absence of quality control and training in rating valuation. The findings have practical implications on rating valuation stakeholders.

PurposeThere are a plethora of putative influencing variables available in the literature for modelling real estate prices using AI. Their choice tends to differ from one researcher to the other, consequently leading to subjectivity in the selection process. Thus, there is a need to seek the viewpoint of practitioners on the applicability and level of significance of these academically established variables.Design/methodology/approachUsing the Delphi technique, this study collated and structured the 35 underlying micro- and macroeconomic parameters derived from literature and eight variables suggested by 11 selected real estate experts. The experts ranked these variables in order of influence using a seven-point Likert scale with a reasonable consensus during the fourth round (Kendall's W = 0.7418).FindingsThe study discovered that 16 variables are very influential with seven being extremely influential. These extremely influential variables include flexibility, adaptability of design, accessibility to the building, the size of office spaces, quality of construction, state of repairs, expected capital growth and proximity to volatile areas.Practical implicationsThe results of this study improve the quality of data available to valuers towards a fortified price prediction for investors, and thereby, restoring the valuers' credibility and integrity.Originality/valueThe “volatility level of an area”, which was revealed as a distinct factor in the survey is used to add to current knowledge concerning office price. Hence, this study offers real estate practitioners and researchers valuable knowledge on the critical variables that must be considered in AI-based price modelling.

It is widely accepted that risk and uncertainty are integral parts of the property valuation process. Uncertainty in property valuation is derived from the characteristics of property itself. The issue pertaining to risk and uncertainty in property valuations is currently one of the key concerns in global valuation practice to date in addressing the decision of risk and uncertainty in valuation, especially for business purposes or in the current term known as business valuation. The judgment and experience still depend on the expertise of the individual valuers alone. The valuation methods used can cause problems if certain elements in business such as risk are highlighted, especially to determine market value. There is a need for valuers to express assumptions which take into account risk and uncertainties, and then pass on the results of the estimation process to the end user of the valuation report. This research employed Analytical Hierarchical Process (AHP) to identify the level of risk in business valuation for valuers to identify which risk areas will expose them to professional liabilities, which then leads to mitigation of risk to determine value in business valuations. AHP will also be able to identify the level of risk in each of the approaches in business valuation which could help valuers to determine the value and market value in the valuation process. This paper will propose some practical approaches of how to address the risk and uncertainty of the valuation process, especially for the purpose of business valuation.

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Property taxation is universal, it is hard to find a country which does not levy a tax on its landed property. The best tax policy in the world is worth little if it cannot be implemented effectively. The level of tax yield of property has been below the expected tax yield due to assessment issue. The function of valuation is to ensure fair assessment of property owners. This paper examines the taxonomy of property tax with a view to understanding it composition and review the impact of valuation accuracy on property tax from existing literature. The study reveals the impact of valuation on property tax, indicating the effects of valuation accuracy on the amount of tax payable or receivable as the case may be. Valuation impact can be viewed as being accurate or inaccurate. When it is accurate the cost is low for the tax administration, low rate of avoidance and evasion, and provision and maintenance of municipal services, but the reverse is the case when it is inaccurate. The study recommends that tax authorities should engage qualified professionals in the assessment of property tax and that regulatory bodies should organise periodic training for their members

A relatively high level of precision is required in real estate valuation for investment purposes. Such estimates of value which is carried out by real estate professionals are relied upon by the end-users of such financial information having paid a certain fee for consultation hence leaving little room for errors. However, valuation reports are often criticised for their inability to be replicated by two or more valuers. Hence, stirring to a keen interest within the academic cycle leading to the need for exploring avenues to improve the price prediction ability of the professional valuer. This study, therefore, focuses on overcoming these challenges by introducing an integrated approach that combines ANFIS with ANN termed ANFIS-AN, thereby having a reiteration in terms of ANN application to fortify price predictability. Using 255 property data alongside 12 variables, the ANFIS-AN model was adopted and its outcome was compared with that of ANN. Finally, the results were subjected to 3 different error testing models using the same training and learning benchmarks. The proposed model’s RMSE gave 1.413169, while that of ANN showed 9.942206. Similarly, using MAPE, ANN recorded 0.256438 while ANFIS-AN had 0.208324. Hence, ANFIS-AN’s performance is laudable, thus a better tool over ANN.

Psychological and behavioural dimensions play a vital role in influencing Valuers’ valuation judgements, thus affecting the validity, accuracy and discrepancy (reliability) of property values. However, behavioural uncertainty research, specifically within the discipline of property valuation among property Valuers, is still limited, particularly in developing countries with their unique property valuation systems, and has been so far conducted in an independent, separate manner, which looks into the behavioural uncertainties non-connectedly. Therefore, this study aims to examine the behavioural uncertainties of local Valuers in property valuation, vitally addressing the questions of behaviours involved and how a behavioural uncertainty is associated with other behavioural issues. This study adopted a phenomenological design, where a session of focus group discussions with 10 public-private Valuers from Johor Bahru, Malaysia was conducted. Results show that local Valuers were subject to various, simultaneous interwoven behavioural uncertainties, which ultimately form a behavioural framework of associations, including biases, client influences, heuristics, professional ethics, and opportunistic behaviours in making their valuation judgement. Biases (subjective preference) and professional ethics (negligence and carelessness) are the two most dominant behaviours involved in local property valuation. These findings provide policy insights to both public and private Valuers, academicians, and the market about the importance of understanding behavioural property economics, that crucially enables them to collectively create a sustainable property valuation environment.